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+---
+comments: true
+difficulty: 中等
+edit_url: https://github.com/doocs/leetcode/edit/main/solution/3200-3299/3230.Customer%20Purchasing%20Behavior%20Analysis/README.md
+---
+
+
+
+# [3230. Customer Purchasing Behavior Analysis 🔒](https://leetcode.cn/problems/customer-purchasing-behavior-analysis)
+
+[English Version](/solution/3200-3299/3230.Customer%20Purchasing%20Behavior%20Analysis/README_EN.md)
+
+## 题目描述
+
+
+
+Table: Transactions
+
+
++------------------+---------+
+| Column Name | Type |
++------------------+---------+
+| transaction_id | int |
+| customer_id | int |
+| product_id | int |
+| transaction_date | date |
+| amount | decimal |
++------------------+---------+
+transaction_id is the unique identifier for this table.
+Each row of this table contains information about a transaction, including the customer ID, product ID, date, and amount spent.
+
+
+Table: Products
+
+
++-------------+---------+
+| Column Name | Type |
++-------------+---------+
+| product_id | int |
+| category | varchar |
+| price | decimal |
++-------------+---------+
+product_id is the unique identifier for this table.
+Each row of this table contains information about a product, including its category and price.
+
+
+Write a solution to analyze customer purchasing behavior. For each customer, calculate:
+
+
+ - The total amount spent.
+ - The number of transactions.
+ - The number of unique product categories purchased.
+ - The average amount spent.
+ - The most frequently purchased product category (if there is a tie, choose the one with the most recent transaction).
+ - A loyalty score defined as: (Number of transactions * 10) + (Total amount spent / 100).
+
+
+Round total_amount, avg_transaction_amount, and loyalty_score to 2 decimal places.
+
+Return the result table ordered by loyalty_score in descending order, then by customer_id in ascending order.
+
+The query result format is in the following example.
+
+
+Example:
+
+
+
Input:
+
+
Transactions table:
+
+
++----------------+-------------+------------+------------------+--------+
+| transaction_id | customer_id | product_id | transaction_date | amount |
++----------------+-------------+------------+------------------+--------+
+| 1 | 101 | 1 | 2023-01-01 | 100.00 |
+| 2 | 101 | 2 | 2023-01-15 | 150.00 |
+| 3 | 102 | 1 | 2023-01-01 | 100.00 |
+| 4 | 102 | 3 | 2023-01-22 | 200.00 |
+| 5 | 101 | 3 | 2023-02-10 | 200.00 |
++----------------+-------------+------------+------------------+--------+
+
+
+
Products table:
+
+
++------------+----------+--------+
+| product_id | category | price |
++------------+----------+--------+
+| 1 | A | 100.00 |
+| 2 | B | 150.00 |
+| 3 | C | 200.00 |
++------------+----------+--------+
+
+
+
Output:
+
+
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+| customer_id | total_amount | transaction_count | unique_categories | avg_transaction_amount | top_category | loyalty_score |
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+| 101 | 450.00 | 3 | 3 | 150.00 | C | 34.50 |
+| 102 | 300.00 | 2 | 2 | 150.00 | C | 23.00 |
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+
+
+
Explanation:
+
+
+ - For customer 101:
+
+ - Total amount spent: 100.00 + 150.00 + 200.00 = 450.00
+ - Number of transactions: 3
+ - Unique categories: A, B, C (3 categories)
+ - Average transaction amount: 450.00 / 3 = 150.00
+ - Top category: C (Customer 101 made 1 purchase each in categories A, B, and C. Since the count is the same for all categories, we choose the most recent transaction, which is category C on 2023-02-10)
+ - Loyalty score: (3 * 10) + (450.00 / 100) = 34.50
+
+
+ - For customer 102:
+
+ - Total amount spent: 100.00 + 200.00 = 300.00
+ - Number of transactions: 2
+ - Unique categories: A, C (2 categories)
+ - Average transaction amount: 300.00 / 2 = 150.00
+ - Top category: C (Customer 102 made 1 purchase each in categories A and C. Since the count is the same for both categories, we choose the most recent transaction, which is category C on 2023-01-22)
+ - Loyalty score: (2 * 10) + (300.00 / 100) = 23.00
+
+
+
+
+
Note: The output is ordered by loyalty_score in descending order, then by customer_id in ascending order.
+
Table: Transactions
+
+
++------------------+---------+
+| Column Name | Type |
++------------------+---------+
+| transaction_id | int |
+| customer_id | int |
+| product_id | int |
+| transaction_date | date |
+| amount | decimal |
++------------------+---------+
+transaction_id is the unique identifier for this table.
+Each row of this table contains information about a transaction, including the customer ID, product ID, date, and amount spent.
+
+
+Table: Products
+
+
++-------------+---------+
+| Column Name | Type |
++-------------+---------+
+| product_id | int |
+| category | varchar |
+| price | decimal |
++-------------+---------+
+product_id is the unique identifier for this table.
+Each row of this table contains information about a product, including its category and price.
+
+
+Write a solution to analyze customer purchasing behavior. For each customer, calculate:
+
+
+ - The total amount spent.
+ - The number of transactions.
+ - The number of unique product categories purchased.
+ - The average amount spent.
+ - The most frequently purchased product category (if there is a tie, choose the one with the most recent transaction).
+ - A loyalty score defined as: (Number of transactions * 10) + (Total amount spent / 100).
+
+
+Round total_amount, avg_transaction_amount, and loyalty_score to 2 decimal places.
+
+Return the result table ordered by loyalty_score in descending order, then by customer_id in ascending order.
+
+The query result format is in the following example.
+
+
+Example:
+
+
+
Input:
+
+
Transactions table:
+
+
++----------------+-------------+------------+------------------+--------+
+| transaction_id | customer_id | product_id | transaction_date | amount |
++----------------+-------------+------------+------------------+--------+
+| 1 | 101 | 1 | 2023-01-01 | 100.00 |
+| 2 | 101 | 2 | 2023-01-15 | 150.00 |
+| 3 | 102 | 1 | 2023-01-01 | 100.00 |
+| 4 | 102 | 3 | 2023-01-22 | 200.00 |
+| 5 | 101 | 3 | 2023-02-10 | 200.00 |
++----------------+-------------+------------+------------------+--------+
+
+
+
Products table:
+
+
++------------+----------+--------+
+| product_id | category | price |
++------------+----------+--------+
+| 1 | A | 100.00 |
+| 2 | B | 150.00 |
+| 3 | C | 200.00 |
++------------+----------+--------+
+
+
+
Output:
+
+
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+| customer_id | total_amount | transaction_count | unique_categories | avg_transaction_amount | top_category | loyalty_score |
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+| 101 | 450.00 | 3 | 3 | 150.00 | C | 34.50 |
+| 102 | 300.00 | 2 | 2 | 150.00 | C | 23.00 |
++-------------+--------------+-------------------+-------------------+------------------------+--------------+---------------+
+
+
+
Explanation:
+
+
+ - For customer 101:
+
+ - Total amount spent: 100.00 + 150.00 + 200.00 = 450.00
+ - Number of transactions: 3
+ - Unique categories: A, B, C (3 categories)
+ - Average transaction amount: 450.00 / 3 = 150.00
+ - Top category: C (Customer 101 made 1 purchase each in categories A, B, and C. Since the count is the same for all categories, we choose the most recent transaction, which is category C on 2023-02-10)
+ - Loyalty score: (3 * 10) + (450.00 / 100) = 34.50
+
+
+ - For customer 102:
+
+ - Total amount spent: 100.00 + 200.00 = 300.00
+ - Number of transactions: 2
+ - Unique categories: A, C (2 categories)
+ - Average transaction amount: 300.00 / 2 = 150.00
+ - Top category: C (Customer 102 made 1 purchase each in categories A and C. Since the count is the same for both categories, we choose the most recent transaction, which is category C on 2023-01-22)
+ - Loyalty score: (2 * 10) + (300.00 / 100) = 23.00
+
+
+
+
+
Note: The output is ordered by loyalty_score in descending order, then by customer_id in ascending order.
+